class_docstr: Zero-padding layer for 1D input (e.g. temporal sequence).
  
  Examples:
  
  >>> input_shape = (2, 2, 3)
  >>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
  >>> x
  [[[ 0  1  2]
    [ 3  4  5]]
   [[ 6  7  8]
    [ 9 10 11]]]
  >>> y = keras.layers.ZeroPadding1D(padding=2)(x)
  >>> y
  [[[ 0  0  0]
    [ 0  0  0]
    [ 0  1  2]
    [ 3  4  5]
    [ 0  0  0]
    [ 0  0  0]]
   [[ 0  0  0]
    [ 0  0  0]
    [ 6  7  8]
    [ 9 10 11]
    [ 0  0  0]
    [ 0  0  0]]]
  
  Args:
      padding: Int, or tuple of int (length 2), or dictionary.
          - If int: how many zeros to add at the beginning and end of
            the padding dimension (axis 1).
          - If tuple of 2 ints: how many zeros to add at the beginning and the
            end of the padding dimension (`(left_pad, right_pad)`).
  
  Input shape:
      3D tensor with shape `(batch_size, axis_to_pad, features)`
  
  Output shape:
      3D tensor with shape `(batch_size, padded_axis, features)`
code: def compute_output_shape(self, input_shape):
      output_shape = list(input_shape)
      if output_shape[1] is not None:
          output_shape[1] += self.padding[0] + self.padding[1]
      return tuple(output_shape)
  
file_path: keras/keras/layers/reshaping/zero_padding1d.py
imports: from keras import ops
  from keras.api_export import keras_export
  from keras.layers.input_spec import InputSpec
  from keras.layers.layer import Layer
  from keras.utils import argument_validation
label_desc: Use image node_id 2 for calling the ZeroPadding1D obj's underlying member method code with example usage: obj.compute_output_shape(input_shape) and returns: tuple